A Military Target Detection Method Based on Improved YOLOv5
To address the problem of false detection and missed detection due to background interference and small scale of military targets in battlefield environment,a military target recognition method CB-YOLOv5 based on improved YOLOv5 is pro-posed.The feature extraction backbone network is reconstructed by using coordinate attention mechanism to enhance the feature ex-traction capability of the network for military targets in complex background.BiFPN is introduced in the feature fusion network to re-duce the loss of shallow feature information and improve the weak targets can be detected.Experiments under the self-built dataset show that the improved algorithm mAP reaches 93.8%,which is 3.5%better than the original model,and can effectively identify multi-scale military targets in the battlefield environment.
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